app.py

import gradio as gr
import pandas as pd
from transformers import pipeline

# ------------------------------------------------
# Load Qwen 3B (instruction-following, concise)
# ------------------------------------------------
generator = pipeline(
    "text-generation",
    model="Qwen/Qwen2.5-3B-Instruct",
    device_map="auto",
    trust_remote_code=True
)

# ------------------------------------------------
# Generic fact quantization (NO domain logic)
# ------------------------------------------------
def magnitude_bucket(x):
    if x < 0.05:
        return "low"
    elif x < 0.2:
        return "medium"
    else:
        return "high"

def direction_bucket(diff):
    if diff > 0:
        return "increase"
    elif diff < 0:
        return "decrease"
    else:
        return "no_change"

# ------------------------------------------------
# Core logic
# ------------------------------------------------
def analyze_kpi(csv_file, top_n):
    df = pd.read_csv(csv_file.name)

    dates = df.columns[1:]
    prev_date, curr_date = dates[-2], dates[-1]

    df["diff"] = df[curr_date] - df[prev_date]
    df["abs_diff"] = df["diff"].abs()

    ranked = df.sort_values("abs_diff", ascending=False)

    top_kpis = ranked.head(3)
    other_kpis = ranked.iloc[3:]

    # -------------------------------
    # Top 3 KPI FACT BLOCKS
    # -------------------------------
    top_facts = []
    for _, row in top_kpis.iterrows():
        top_facts.append({
            "KPI": row["Kpi"],
            "DIRECTION": direction_bucket(row["diff"]),
            "CHANGE_VALUE": round(row["abs_diff"], 2),
            "MAGNITUDE": magnitude_bucket(row["abs_diff"]),
            "UNIT": "percentage points" if "%" in row["Kpi"] else "units"
        })

    # -------------------------------
    # Remaining KPI AGGREGATED FACTS
    # -------------------------------
    if len(other_kpis) > 0:
        other_facts = {
            "KPI_COUNT": len(other_kpis),
            "AVG_CHANGE": round(other_kpis["abs_diff"].mean(), 2),
            "MAGNITUDE": magnitude_bucket(other_kpis["abs_diff"].mean()),
            "DIRECTION_BALANCE": (
                "mostly_increase" if (other_kpis["diff"] > 0).mean() > 0.7
                else "mostly_decrease" if (other_kpis["diff"] > 0).mean() < 0.3
                else "mixed"
            )
        }
    else:
        other_facts = None

    # -------------------------------
    # Model input = FACT BLOCK ONLY
    # (no narrative text embedded)
    # -------------------------------
    model_input = (
        "Write a short insight-style summary using only the facts below.\n\n"
        "TOP_KPI_FACTS:\n"
        f"{top_facts}\n\n"
        "OTHER_KPI_FACTS:\n"
        f"{other_facts}"
    )

    # -------------------------------
    # Controlled variation generation
    # -------------------------------
    output = generator(
        model_input,
        max_new_tokens=120,
        do_sample=True,
        temperature=0.6,
        top_p=0.85,
        repetition_penalty=1.1
    )[0]["generated_text"]

    return ranked.head(top_n)[["Kpi", "abs_diff"]], output

# ------------------------------------------------
# Gradio UI (Hugging Face Space)
# ------------------------------------------------
with gr.Blocks(title="Network Insight Summary") as demo:
    gr.Markdown("## 📊 Network Insight Summary")
    gr.Markdown(
        "Upload KPI CSV to generate a concise, insight-style summary "
        "highlighting the most significant KPI changes."
    )

    csv_input = gr.File(file_types=[".csv"])
    top_n_input = gr.Slider(
        minimum=3,
        maximum=6,
        value=5,
        step=1,
        label="KPIs to Display"
    )

    btn = gr.Button("Generate Insight")

    table = gr.Dataframe(label="Top KPI Changes")
    summary = gr.Textbox(label="Insight Summary", lines=4)

    btn.click(
        analyze_kpi,
        inputs=[csv_input, top_n_input],
        outputs=[table, summary]
    )

demo.launch()